图书情报工作 ›› 2021, Vol. 65 ›› Issue (23): 58-69.DOI: 10.13266/j.issn.0252-3116.2021.23.007

• 情报研究 • 上一篇    下一篇

专家个体多维特征刻画与专家组均衡推荐研究

华斌, 吴诺, 贺欣   

  1. 天津财经大学理工学院 天津 300222
  • 收稿日期:2021-05-04 修回日期:2021-08-23 出版日期:2021-12-05 发布日期:2021-12-18
  • 作者简介:华斌,教授,博士,E-mail:bigsoon@sina.com;吴诺,高级实验师,博士研究生;贺欣,硕士研究生。
  • 基金资助:
    本文系天津市信息化专项资金项目"电子政务项目全流程管理信息系统建设与软件项目预算评价系统"(项目编号:津党网信函(2018)146号)研究成果之一。

Research on Expert Individuals Multi-Feature Depiction and Expert Group Equilibrium Recommendation

Hua Bin, Wu Nuo, He Xin   

  1. School of Science and Technology, Tianjin University of Finance and Economics, Tianjin 300222
  • Received:2021-05-04 Revised:2021-08-23 Online:2021-12-05 Published:2021-12-18

摘要: [目的/意义]提出一种基于专家个体多维特征刻画的电子政务项目评审专家组推荐方法,提升专家组间项目评审的一致性水平。[方法/过程]以专家个体的长期评审意见为数据源,利用意见挖掘技术实现知识元识别与情感极性获取;构造专家的领域知识结构并动态迭代更新;利用统计分析刻画专家知识水平、评审深刻性、情感风格、领域专长特征,实现基于科学计量的专家特征刻画并以此为基础进行专家组合的推荐。[结果/结论]本文的方法注重专家组的多维特征均衡,对电子政务项目评审具有很好的问题针对性,并在实践中取得了良好的应用效果。

关键词: 电子政务, 项目管理, 意见挖掘, 知识单元计量, 专家推荐

Abstract: [Purpose/significance] A recommendation method of e-government project review expert group based on multi-feature depiction of individual experts is proposed. It can improve the consistency level of project evaluation among expert groups.[Method/process] Taking the long-term evaluation opinions of individual experts as the data source, knowledge element recognition and emotion polarity acquisition were realized by using opinion mining technology. The domain knowledge structure of experts was constructed and updated dynamically. Statistical analysis was used to describe level of expert knowledge, judging depth, emotional characteristics and domain expertise. This paper described the expert feature based on Scientometrics and recommended a combination of experts.[Result/conclusion] The method in this paper focuses on the multi-dimensional feature equilibrium of expert group. It has good pertinence for e-government project evaluation, and achieves good application effects.

Key words: e-government, project management, opinion mining, knowledge unit measurement, expert recommendation

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